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Aner Zakobar
2026-06-19 18:09:39 +03:00
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* impl
ROLL (Ranking via Optimized Label Learning) — PyTorch research project implementing custom loss functions for binary classification using kernel density estimation (KDE) to optimize TPR at target FPR thresholds. Targets imbalanced classification problems.
ROLL (Rate Optimized Likelyhood-based Loss) — PyTorch research project implementing custom loss functions for binary classification using kernel density estimation (KDE) to optimize TPR at target FPR thresholds. Targets imbalanced classification problems.
** Architecture
@@ -42,3 +42,4 @@ ROLL (Ranking via Optimized Label Learning) — PyTorch research project impleme
- [[id:001430d5-e1e7-4e72-baf6-17399bfd6447][impl/loss-functions]] — Loss variants, KDE internals, gradient computation
- [[id:a53cbe84-cd8d-45c2-a8cf-34ab520a3ea5][impl/experiments]] — Experiment structure, training flow, metrics, output layout
- [[id:b8a9886a-d349-43e5-a745-817a148c1fd8][impl/datasets]] — Dataset catalog, KEEL list, eval metrics
- [[id:151d5686-6f40-4158-a59a-b0be94cdc969][impl/research]] — Literature survey: competing methods, dataset gaps, key papers